用程序VB实现电磁波测距观测结果的化算

由于电磁波测距与气象、测区基准面高程以及仪器参数等有关,因此需对测距观测结果进行改正,通过介绍用Visua1Basic语言编程进行测距观测结果化算的方法.该方法己在实践中广泛运用,大大提高了工作效率.

An Improved Differential Evolution for Optimization of Chemical Process

Differential evolution （DE） is an evolutionary optimization method, which has been successfully used in many practical cases. However, DE involves large computation time, especially, when used to optimize the compurationally expensive objective function. To overcome this .difficulty, the concept of immunity based on vaccination is used to help proliferate excellent schemata and to restrain the degenerate phenomenon. To improve the effective- ness of vaccines, a new vaccine autonomous obtaining method, and a method of deciding the probability of vacci- nation are proposed. In addition, a method for modifying the search space dynamically is proposed to enhance the possibility of converging to the true global optimum. Experiments showed that the improved DE performs better than the classical DE significantly.

Chemical process dynamic optimization based on hybrid differential evolution algorithm integrated with Alopex

To solve dynamic optimization problem of chemical process (CPDOP), a hybrid differential evolution algorithm, which is integrated with Alopex and named as Alopex-DE, was proposed. In Alopex-DE, each original individual has its own symbiotic individual, which consists of control parameters. Differential evolution operator is applied for the original individuals to search the global optimization solution. Alopex algorithm is used to co-evolve the symbiotic individuals during the original individual evolution and enhance the fitness of the original individuals. Thus, control parameters are self-adaptively adjusted by Alopex to obtain the real-time optimum values for the original population. To illustrate the whole performance of Alopex-DE, several varietal DEs were applied to optimize 13 benchmark functions. The results show that the whole performance of Alopex-DE is the best. Further, Alopex-DE was applied to solve 4 typical CPDOPs, and the effect of the discrete time degree on the optimization solution was analyzed. The satisfactory result is obtained.

Computational Mass Transfer Method for Chemical Process Simulation

The recent works on the development of computational mass transfer （CMT） method and its applications in chemical process simulation are reviewed. Some development strategies and challenges in future research are also discussed.

Multi-objective optimization for leaching process using improved two-stage guide PSO algorithm

A mathematical mechanism model was proposed for the description and analysis of the heat-stirring-acid leaching process.The model is proved to be effective by experiment.Afterwards,the leaching problem was formulated as a constrained multi-objective optimization problem based on the mechanism model.A two-stage guide multi-objective particle swarm optimization(TSG-MOPSO) algorithm was proposed to solve this optimization problem,which can accelerate the convergence and guarantee the diversity of pareto-optimal front set as well.Computational experiment was conducted to compare the solution by the proposed algorithm with SIGMA-MOPSO by solving the model and with the manual solution in practice.The results indicate that the proposed algorithm shows better performance than SIGMA-MOPSO,and can improve the current manual solutions significantly.The improvements of production time and economic benefit compared with manual solutions are 10.5% and 7.3%,respectively.

Calculation of Metzner Constant for Double Helical Ribbon Impeller by Computational Fluid Dynamic Method

Using the multiple reference frames （MRF） impeller method, the three-dimensional non-Newtonian flow field generated by a double helical ribbon （DHR） impeller has been simulated. The velocity field calculated by the numerical simulation was similar to the previous studies and the power constant agreed well with the experimental data. Three computational fluid dynamic （CFD） methods, labeled Ⅰ, Ⅱ and Ⅲ, were used to compute the Metzuer constant k5. The results showed that the calculated value from the slop method （method Ⅰ） was consistent with the experimental data. Method Ⅱ, which took the maximal circumference-average shear rate around the impeller as the effective shear rate to compute ks, also showed good agreement with the experiment. However, both methods suffer from the complexity of calculation procedures. A new method （method Ⅲ） was devised in this paper to use the area-weighted average viscosity around the impeller as the effective viscosity for calculating k5. Method Ⅲ showed both good accuracy and ease of use.

Multi-objective optimization of p-xylene oxidation process using an improved self-adaptive differential evolution algorithm

The rise in the use of global polyester fiber contributed to strong demand of the Terephthalic acid （TPA）. The liquid-phase catalytic oxidation of p-xylene （PX） to TPA is regarded as a critical and efficient chemical process in industry [ 1 ]. PX oxidation reaction involves many complex side reactions, among which acetic acid combustion and PX combustion are the most important. As the target product of this oxidation process, the quality and yield of TPA are of great concern. However, the improvement of the qualified product yield can bring about the high energy consumption, which means that the economic objectives of this process cannot be achieved simulta- neously because the two objectives are in conflict with each other. In this paper, an improved self-adaptive multi-objective differential evolution algorithm was proposed to handle the multi-objective optimization prob- lems. The immune concept is introduced to the self-adaptive multi-objective differential evolution algorithm （SADE） to strengthen the local search ability and optimization accuracy. The proposed algorithm is successfully tested on several benchmark test problems, and the performance measures such as convergence and divergence metrics are calculated. Subsequently, the multi-objective optimization of an industrial PX oxidation process is carried out using the proposed immune self-adaptive multi-objective differential evolution algorithm （ISADE）. Optimization results indicate that application oflSADE can greatly improve the yield of TPA with low combustion loss without degenerating TA quality.

Thermodynamics of chromite ore oxidative roasting process

To explicate the thermodynamics of the chromite ore lime-free roasting process, the thermodynamics of reactions involved in this process was calculated and the phrases of sinter with different roasting times were studied. The thermodynamics calculation shows that all the standard Gibbs free energy changes of the reactions to form Na2CrO4, Na2O-Fe2O3, Na2O·Al2O3 and Na2O3 SiO2 via chromite ore and Na2CO3 are negative, and the standard Gibbs free energy changes of the reactions between MgO, Fe2O3 and SiO2 released from chromite spinel to form MgO-Fe2O3 and MgO·SiO2 are also negative at the oxidative roasting temperatures （1 173 1 473 K）. The phrase analysis of the sinter in lime-free roasting process shows that Na2O·Fe2O3, Na2O·Al2O3 and Na2O·SiO2 can be formed in the first 20 min, but they decrease in contents and finally disappear with the increase of roasting time. The final phase compositions of the sinter are Na2CrO4, MgO·Fe2O3, MgO·SiO2 and MgO. The results indicate that Na2CrO4 can be formed easily via the reaction ofNa2CO3 with chromite ore. Na2O·Fe2O3, Na2O-Al2O3 and Na2O·SiO2 can be formed as intermediate compounds in the roasting process and they can further react with chromite ore to form Na2CrO4. MgO released from chromite ore may react with iron oxides and silicon oxide to form stable compounds of MgO·Fe2O3 and MgO·SiO2, respectively.

Transformation of microstructure and phase of disodium guanosine 5′-monophosphate: Thermodynamic perspectives

Microstructure and phase transformation of disodium guanosine 5′-monophosphate(5′-GMPNa_2) are extremely important for controlling the process and understanding the mechanism of crystallization. In this work, the thermodynamic properties of polymorphous 5′-GMPNa_2 especially the solubility were studied, the solubility results show that 5′-GMPNa_2 is more soluble in ethanol–water(E–W) than in isopropanol–water(I–W). The amorphous form of 5′-GMPNa_2 is more soluble than the crystalline form at the same mole fraction and temperature. Meanwhile, the crystalline forms and morphologies of the residual solids were characterized by PXRD and SEM. The results indicate that solid forms of 5′-GMPNa_2 transformed spontaneously from amorphous to crystalline when the ethanol proportion is ≥20%. In addition, increasing the pH facilitates the dissolution of 5′-GMPNa_2 and helps to maintain the crystalline form. The associated Gibbs free energy values were calculated to verify the trend of transformation from amorphous to crystalline 5′-GMPNa_2. These results should help to guide the industrial crystallization process and to obtain the crystalline form of 5′-GMPNa_2.

Run-to-run Optimization for Fed-batch Fermentation Process with Swarm Energy Conservation Particle Swarm Optimization Algorithm

An iterative optimization strategy for fed-batch fermentation process is presented by combining a run-to-run optimization with swarm energy conservation particle swarm optimization (SEC-PSO). SEC-PSO, which is designed with the concept of energy conservation, can solve the problem of premature convergence frequently appeared in standard PSO algorithm by partitioning its population into several sub-swarms according to the energy of the swarm and is used in the optimization strategy for parameter identification and operation condition optimization. The run-to-run optimization exploits the repetitive nature of fed-batch processes in order to deal with the optimal problems of fed-batch fermentation process with inaccurate process model and unsteady process state. The kinetic model parameters, used in the operation condition optimization of the next run, are adjusted by calculating time-series data obtained from real fed-batch process in the run-to-run optimization. The simulation results show that the strategy can adjust its kinetic model dynamically and overcome the instability of fed-batch process effectively. Run-to-run strategy with SEC-PSO provides an effective method for optimization of fed-batch fermentation process.

Multi-objective Evolutionary Algorithms for MILP and MINLP in Process Synthesis

Steady-state non-dominated sorting genetic algorithm (SNSGA), a new form of multi-objective genetic algorithm, is implemented by combining the steady-state idea in steady-state genetic algorithms (SSGA) and the fitness assignment strategy of non-dominated sorting genetic algorithm (NSGA). The fitness assignment strategy is improved and a new self-adjustment scheme of is proposed. This algorithm is proved to be very efficient both computationally and in terms of the quality of the Pareto fronts produced with five test problems including GA difficult problem and GA deceptive one. Finally, SNSGA is introduced to solve multi-objective mixed integer linear programming (MILP) and mixed integer non-linear programming (MINLP) problems in process synthesis.

A Comparison of Arithmetic Operations for Dynamic Process Optimization Approach

A comparison of arithmetic operations of two dynamic process optimization approaches called quasi-sequential approach and reduced Sequential Quadratic Programming(rSQP)simultaneous approach with respect to equality constrained optimization problems is presented.Through the detail comparison of arithmetic operations,it is concluded that the average iteration number within differential algebraic equations(DAEs)integration of quasi-sequential approach could be regarded as a criterion.One formula is given to calculate the threshold value of average iteration number.If the average iteration number is less than the threshold value,quasi-sequential approach takes advantage of rSQP simultaneous approach which is more suitable contrarily.Two optimal control problems are given to demonstrate the usage of threshold value.For optimal control problems whose objective is to stay near desired operating point,the iteration number is usually small.Therefore,quasi-sequential approach seems more suitable for such problems.

Application of a Derivative-Free Method with Projection Skill to Solve an Optimization Problem

Improving numerical forecasting skill in the atmospheric and oceanic sciences by solving optimization problems is an important issue. One such method is to compute the conditional nonlinear optimal perturbation(CNOP), which has been applied widely in predictability studies. In this study, the Differential Evolution(DE) algorithm, which is a derivative-free algorithm and has been applied to obtain CNOPs for exploring the uncertainty of terrestrial ecosystem processes, was employed to obtain the CNOPs for finite-dimensional optimization problems with ball constraint conditions using Burgers' equation. The aim was first to test if the CNOP calculated by the DE algorithm is similar to that computed by traditional optimization algorithms, such as the Spectral Projected Gradient(SPG2) algorithm. The second motive was to supply a possible route through which the CNOP approach can be applied in predictability studies in the atmospheric and oceanic sciences without obtaining a model adjoint system, or for optimization problems with non-differentiable cost functions. A projection skill was first explanted to the DE algorithm to calculate the CNOPs. To validate the algorithm, the SPG2 algorithm was also applied to obtain the CNOPs for the same optimization problems. The results showed that the CNOPs obtained by the DE algorithm were nearly the same as those obtained by the SPG2 algorithm in terms of their spatial distributions and nonlinear evolutions. The implication is that the DE algorithm could be employed to calculate the optimal values of optimization problems, especially for non-differentiable and nonlinear optimization problems associated with the atmospheric and oceanic sciences.

A Hybrid Improved Genetic Algorithm and Its Application in Dynamic Optimization Problems of Chemical Processes

The solutions of dynamic optimization problems are usually very difficult due to their highly nonlinear and multidimensional nature. 13enetic algorithm （GA） has been proved to be a teasibte method when the gradient is difficult to calculate. Its advantage is that the control profiles at all time stages are optimized simultaneously, but its convergence is very slow in the later period of evolution and it is easily trapped in the local optimum. In this study, a hybrid improved genetic algorithm （HIGA） for solving dynamic optimization problems is proposed to overcome these defects. Simplex method （SM） is used to perform the local search in the neighborhood of the optimal solution. By using SM, the ideal searching direction of global optimal solution could be found as soon as possible and the convergence speed of the algorithm is improved. The hybrid algorithm presents some improvements, such as protecting the best individual, accepting immigrations, as well as employing adaptive crossover and Ganssian mutation operators. The efficiency of the proposed algorithm is demonstrated by solving several dynamic optimization problems. At last, HIGA is applied to the optimal production of secreted protein in a fed batch reactor and the optimal feed-rate found by HIGA is effective and relatively stable.

Microstructure evolution and Al_3(Sc_(1-x)Zr_x) precipitates' kinetics in Al-Zn-Mg alloy during homogenization

The microstructure evolutions of two A1-Zn-Mg alloys, one of which was alloyed with Sc and Zr, and the kinetics of A13（SCl-xZrx） precipitates in the A1-Zn-Mg alloy during homogenization were investigated. Both alloys under as-cast condition with supersaturated, non-equilibrium T（Mg32（A1, Zn）49） phase and impurities phase were displayed. When the homogenization temperatures are below 350 ~C, Zn and Mg atoms precipitate from matrix; however, when the temperatures are above 400 ~C, T phase dissolves into matrix, enhancing solid-solution strengthening. Kinetics of A13（Scl.xZrx） precipitates was studied based on Jmat Pro software calculation and the difference values between the hardness of the two alloys in each homogenization condition. The calculations predict that the Sc and Zr solubilities in ct-A1 decline with the presence of Mg and Zn. Investigation of the difference values reveals that when the temperature is between 300 ~C and 350 ~C, the nucleation rate of A13（Sc1-xZrx） precipitates is the highest and the strengthening effect from A13（SCl_xZrx） precipitates is the best. After homogenization at 470℃ for 12 h, non-equilibrium T phase disappears, while impurity phase remains. The mean diameter of A13（Scl_xZrx） precipitates is around 18 urn. Ideas about better fulfilling the potentials of Sc and Zr were proposed at last.

Fault Prediction Based on Dynamic Model and Grey Time Series Model in Chemical Processes

This paper combines grey model with time series model and then dynamic model for rapid and in-depth fault prediction in chemical processes. Two combination methods are proposed. In one method, historical data is introduced into the grey time series model to predict future trend of measurement values in chemical process. These predicted measurements are then used in the dynamic model to retrieve the change of fault parameters by model based diagnosis algorithm. In another method, historical data is introduced directly into the dynamic model to retrieve historical fault parameters by model based diagnosis algorithm. These parameters are then predicted by the grey time series model. The two methods are applied to a gravity tank example. The case study demonstrates that the first method is more accurate for fault prediction.

Adaptive bands filter bank optimized by genetic algorithm for robust speech recognition system

Perceptual auditory filter banks such as Bark-scale filter bank are widely used as front-end processing in speech recognition systems.However,the problem of the design of optimized filter banks that provide higher accuracy in recognition tasks is still open.Owing to spectral analysis in feature extraction,an adaptive bands filter bank (ABFB) is presented.The design adopts flexible bandwidths and center frequencies for the frequency responses of the filters and utilizes genetic algorithm (GA) to optimize the design parameters.The optimization process is realized by combining the front-end filter bank with the back-end recognition network in the performance evaluation loop.The deployment of ABFB together with zero-crossing peak amplitude (ZCPA) feature as a front process for radial basis function (RBF) system shows significant improvement in robustness compared with the Bark-scale filter bank.In ABFB,several sub-bands are still more concentrated toward lower frequency but their exact locations are determined by the performance rather than the perceptual criteria.For the ease of optimization,only symmetrical bands are considered here,which still provide satisfactory results.

Local inhomogeneity in totally asymmetric simple exclusion processes with different hopping rates

Local inhomogeneity in totally asymmetric simple exclusion processes （TASEPs） with different hopping rates was studied. Many biological and chemical phenomena can be described by these non-equilibrium processes. A simple approximate theory and extensive Monte Carlo computer simulations were used to calculate the steady-state phase diagrams and bulk densities. It is found that the phase diagram for local inhomogeneity in TASEP with different hopping rates p is qualitatively similar to homogeneous models. Interestingly, there is a saturation point pair （a＊, fl＊） for the system, which is decided by parameters p and q. There are three stationary phases in the system, when parameter p is fixed （i.e., p=0.8）, with the increase of the parameter q, the region of LD/LD and HD/HD phase increases and the HD/LD is the only phase which the region shrinks. The analytical results are in good agreement with simulations.

The Convergence of the Abstract Evolutionary Algorithm Based on a Special Selection Mechanism

There has been a growing interest in mathematical models to character the evolutionary algorithms. The best-known one of such models is the axiomatic model colled the abstract evolutionary algorithm. In this paper, we first introduce the definitions of the abhstract selection and evolution operators, and that of the abstract evolutionary algorithm, which describes the evolution as an abstract stochastic process composed of these two fundamental abstract operators. In particular, a kind of abstract evolutionary algorithms based on a special selection mechansim is discussed. According to the sorting for the state space, the properties of the single step transition matrix for the algorithm are anaylzed. In the end, we prove that the limit probability distribution of the Markov chains exists. The present work provides a big step toward the establishment of a unified theory of evolutionary computation.

Prediction of Critical Endpoints Based on the PR Equation of State

In the design of chemical processes,such as catalytic cracking of bitumen and heavy oil,the knowledge of phase behavior at the critical endpoint is essential.Based on the PR equation of state,the algorithm developed by Heidemann and Khalil for calculating critical properties was used to compute critical points.An algorithm for determining the equilibrium phase of the critical point using the tangent plane criterion was developed,and was used to calculate the critical endpoints of different mixtures,including non-polar,polar and associating systems.The critical endpoint,representing the type of the phase behavior,was employed to fit the interaction parameter of mixtures in critical state at high pressure.Lines of critical endpoints for ternary mixtures were also determined with the algorithm.